Sentence embedding
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In natural language processing, a sentence embedding refers to a numeric representation of a sentence in the form of a vector of real numbers which encodes meaningful semantic information.[1][2][3][4][5][6][7][8]
State of the art embeddings are based on the learned hidden layer representation of dedicated sentence transformer models. BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence and can be fine-tuned for use in sentence classification tasks. In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance[9] by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset.
Other approaches are loosely based on the idea of distributional semantics applied to sentences. Skip-Thought trains an encoder-decoder structure for the task of neighboring sentences predictions. Though this has been shown to achieve worse performance than approaches such as InferSent or SBERT.
An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW).[10] However, more elaborate solutions based on word vector quantization have also been proposed. One such approach is the vector of locally aggregated word embeddings (VLAWE),[11] which demonstrated performance improvements in downstream text classification tasks.
Applications
In recent years, sentence embedding has seen a growing level of interest due to its applications in natural language queryable knowledge bases through the usage of vector indexing for semantic search. LangChain for instance utilizes sentence transformers for purposes of indexing documents. In particular, an indexing is generated by generating embeddings for chunks of documents and storing (document chunk, embedding) tuples. Then given a query in natural language, the embedding for the query can be generated. A top k similarity search algorithm is then used between the query embedding and the document chunk embeddings to retrieve the most relevant document chunks as context information for question answering tasks. This approach is also known formally as retrieval augmented generation[12]
Though not as predominant as BERTScore, sentence embeddings are commonly used for sentence similarity evaluation which sees common use for the task of optimizing a Large language model's generation parameters is often performed via comparing candidate sentences against reference sentences. By using the cosine-similarity of the sentence embeddings of candidate and reference sentences as the evaluation function, a grid-search algorithm can be utilized to automate hyperparameter optimization.
Evaluation
A way of testing sentence encodings is to apply them on Sentences Involving Compositional Knowledge (SICK) corpus[13] for both entailment (SICK-E) and relatedness (SICK-R).
In [14] the best results are obtained using a BiLSTM network trained on the Stanford Natural Language Inference (SNLI) Corpus. The Pearson correlation coefficient for SICK-R is 0.885 and the result for SICK-E is 86.3. A slight improvement over previous scores is presented in:[15] SICK-R: 0.888 and SICK-E: 87.8 using a concatenation of bidirectional Gated recurrent unit.
See also
External links
- InferSent sentence embeddings and training code
- Universal Sentence Encoder
- Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
References
- ↑ Paper Summary: Evaluation of sentence embeddings in downstream and linguistic probing tasks
- ↑ Barkan, Oren; Razin, Noam; Malkiel, Itzik; Katz, Ori; Caciularu, Avi; Koenigstein, Noam (2019). "Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding". arXiv:1908.05161 [cs.LG].
- ↑ The Current Best of Universal Word Embeddings and Sentence Embeddings
- ↑ Cer, Daniel; Yang, Yinfei; Kong, Sheng-yi; Hua, Nan; Limtiaco, Nicole; John, Rhomni St.; Constant, Noah; Guajardo-Cespedes, Mario; Yuan, Steve; Tar, Chris; Sung, Yun-Hsuan; Strope, Brian; Kurzweil, Ray (2018). "Universal Sentence Encoder". arXiv:1803.11175 [cs.CL].
- ↑ Wu, Ledell; Fisch, Adam; Chopra, Sumit; Adams, Keith; Bordes, Antoine; Weston, Jason (2017). "StarSpace: Embed All the Things!". arXiv:1709.03856 [cs.CL].
- ↑ Sanjeev Arora, Yingyu Liang, and Tengyu Ma. "A simple but tough-to-beat baseline for sentence embeddings.", 2016; openreview:SyK00v5xx.
- ↑ Trifan, Mircea; Ionescu, Bogdan; Gadea, Cristian; Ionescu, Dan (2015). "A graph digital signal processing method for semantic analysis". 2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics. pp. 187–192. doi:10.1109/SACI.2015.7208196. ISBN 978-1-4799-9911-8.
- ↑ Basile, Pierpaolo; Caputo, Annalina; Semeraro, Giovanni (2012). "A Study on Compositional Semantics of Words in Distributional Spaces". 2012 IEEE Sixth International Conference on Semantic Computing. pp. 154–161. doi:10.1109/ICSC.2012.55. ISBN 978-1-4673-4433-3.
- ↑ Reimers, Nils; Gurevych, Iryna (2019). "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks". arXiv:1908.10084 [cs.CL].
- ↑ Mikolov, Tomas; Chen, Kai; Corrado, Greg; Dean, Jeffrey (2013-09-06). "Efficient Estimation of Word Representations in Vector Space". arXiv:1301.3781 [cs.CL].
- ↑ Ionescu, Radu Tudor; Butnaru, Andrei (2019). "Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation". Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (Minneapolis, Minnesota: Association for Computational Linguistics): 363–369. doi:10.18653/v1/N19-1033. https://aclanthology.org/N19-1033.
- ↑ Lewis, Patrick; Perez, Ethan; Piktus, Aleksandra; Petroni, Fabio; Karpukhin, Vladimir; Goyal, Naman; Küttler, Heinrich; Lewis, Mike; Yih, Wen-tau; Rocktäschel, Tim; Riedel, Sebastian; Kiela, Douwe (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks". arXiv:2005.11401 [cs.CL].
- ↑ Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, and Roberto Zamparelli. "A SICK cure for the evaluation of compositional distributional semantic models." In LREC, pp. 216-223. 2014 [1].
- ↑ Conneau, Alexis; Kiela, Douwe; Schwenk, Holger; Barrault, Loic; Bordes, Antoine (2017). "Supervised Learning of Universal Sentence Representations from Natural Language Inference Data". arXiv:1705.02364 [cs.CL].
- ↑ Subramanian, Sandeep; Trischler, Adam; Bengio, Yoshua; Christopher J Pal (2018). "Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning". arXiv:1804.00079 [cs.CL].
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